Libraries

library(labelled)# labelisation
library(tidyverse)
library(readxl)# lecture des fichiers excels
library(sjPlot)#création de graphiques descriptifs
library(dplyr)
library(gtsummary)
library(plotly) # graphique

1. Importation et mise en forme

Repertoire de travail

setwd(getwd())

Importation des bases de données

data1 <- read_csv("annual-growth-in-gni-per-capita.csv")
data2 <- read_csv("gender-inequality-index.csv")
data3 <- read_csv("population-growth-annual.csv")

Sélection des pays de l’afrique de l’ouest

west_africa <- c("Togo","Benin","Senegal","Niger","Mali","Ghana",
                 "Nigeria","Burkina Faso","Guinea","Guinea-Bissau",
                 "Sierra Leone","Cabo Verde","Gambia","Côte d’Ivoire","Mauritania")

Graphique I

Aggrégation des données et fusion

#Monde#
world <- data1 %>%
  group_by(`Start Year`) %>%
  summarise(Value = mean(Value))
#Niger#
niger <- data1 %>%
  filter(`Region Name` == "Niger") %>%
  group_by(`Start Year`) %>%
  summarise(Value = Value)
#Afrique de l'ouest#
West_Africa <- data1 %>%
  filter(`Region Name` %in%west_africa) %>%
  group_by(`Start Year`) %>%
  summarise(Value = mean(Value))
world$Region.Name <- "World"
niger$Region.Name <- "Niger"
West_Africa$Region.Name <- "Western Africa"

Fusion des bases de données

base <- rbind(rbind(world, niger),West_Africa)

Premier graphique

ggplot(base) +
  aes(x  = `Start Year`, y = Value, colour = Region.Name) +
  # ajout des points
  geom_point(shape = "circle", size = 2) + 
  #Ajout de la courbe reliant les points
  geom_line(size = 0.7,show.legend=NULL) +  
  scale_fill_hue(direction = 1.2) +
  #Couleurs des différentes courbes
  scale_color_manual(
  values = c(Niger = "#85C1E9",
  `Western Africa` = "#5B5B5C",
  World = "#B2BABB")
  )+
  # Titres
  labs(title = "Figure : Income growth and distribution (Gini Index)",
       caption = "Source : World Bank", x=NULL, y=NULL, color = NULL) +
  # Echelles des axes en abscisses
  scale_x_continuous(breaks=seq(from=1970, to = 2021, by=10))+
  # Echelles des axes en ordonnées
  scale_y_continuous(breaks = seq(from=-7, to = 10, by=1.5), expand = c(0,0))+
  ylim(-7,10)+
  # Thèmes du fond
  theme_minimal()+
  theme(
    # Paramètre du titre, couleur, police, position
    plot.title = element_text(colour = "#0F4761", face = "italic", size=9, vjust = 8),
    # Paramètre de la legende, taille...
    legend.text = element_text(size = 8), 
    # Position de la legende
    legend.position = c(0.13, 1.03),
    legend.direction = "horizontal", 
    # Paramètres pour la source, position...
    plot.caption = element_text(hjust = 0),
    # Effacer les petites lignes du cadran
    panel.grid.minor = element_blank(),
    #  lignes horizontales 
    panel.grid.major.y = element_line(linetype = "dashed"),
    #  cadran
    plot.margin = margin(30,30,30,30))

## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.

# Graphique II

Aggrégation des données et fusion

#Monde#
world <- data3 %>%
  group_by(`Start Year`) %>%
  summarise(Value = mean(Value))
#Niger#
niger <- data3 %>%
  filter(`Region Name` == "Niger") %>%
  group_by(`Start Year`) %>%
  summarise(Value = Value)
#Afrique de l'ouest#
West_Africa <- data3 %>%
  filter(`Region Name` %in%west_africa) %>%
  group_by(`Start Year`) %>%
  summarise(Value = mean(Value))

world$Region.Name <- "World"
niger$Region.Name <- "Niger"
West_Africa$Region.Name <- "Western Africa"

Fusion des bases de données

base <- rbind(rbind(world, niger),West_Africa)

ggplot(base) +
  aes(x  = `Start Year`, y = Value, colour = Region.Name) +
  # ajout des points
  geom_point(shape = "circle", size = 2) + 
  #Ajout de la courbe reliant les points
  geom_line(size = 0.58, show.legend = NULL) +  
  scale_fill_hue(direction = 1) +
  #Couleurs des différentes courbes
  scale_color_manual(
  values = c(Niger = "#85C1E9",
  `Western Africa` = "#5B5B5C",
  World = "#B2BABB")
  )+
  # Titres
  labs(title = "Figure : Annual population growth (%)",
       caption = "Source : World Bank", x=NULL, y=NULL, color = NULL) +
  # Echelles des axes en abscisses
  scale_x_continuous(breaks=seq(from=1970, to = 2021, by=10))+
  # Echelles des axes en ordonnées
  scale_y_continuous(breaks = seq(from=0, to = 4, by=1.5), expand = c(0,0))+ #l'echelle des axes
  ylim(0,4)+
  # Thèmes du fond
  theme_minimal()+
  theme(
    # Paramètre du titre, couleur, police, position
    plot.title = element_text(colour = "#0F4761", face = "italic", size=9, vjust = 8),
    # Paramètre de la legende, taille...
    legend.text = element_text(size = 8), 
    # Position de la legende
    legend.position = c(0.132, 1.03),
    legend.direction = "horizontal", 
    # Paramètres pour la source, position...
    plot.caption = element_text(hjust = 0),
    # Effacer les petites lignes du cadran
    panel.grid.minor = element_blank(),
    # Mettre les lignes horizontales en forme de tirets
    panel.grid.major.y = element_line(linetype = "dashed"),
    # Marge pour le cadran
    plot.margin = margin(30,30,30,30))

# Graphique III

Aggrégation des données et fusion

#Monde#
world <- data2 %>%
  group_by(`Start Year`) %>%
  summarise(Value = mean(Value))
#Niger#
niger <- data2 %>%
  filter(`Region Name` == "Niger") %>%
  group_by(`Start Year`) %>%
  summarise(Value = Value)
#Afrique de l'ouest#
West_Africa <- data2 %>%
  filter(`Region Name` %in%west_africa) %>%
  group_by(`Start Year`) %>%
  summarise(Value = mean(Value))
world$Region.Name <- "World"
niger$Region.Name <- "Niger"
West_Africa$Region.Name <- "Western Africa"

Fusion des bases de données

base <- rbind(rbind(world, niger),West_Africa)

Graphique

library(ggpp) 
specific_value <- base[base$`Start Year` == 2010 & base$Region.Name == "World", ]

# Graphique ggplot avec geom_label_s
ggplot(base) +
  aes(x = `Start Year`, y = Value, colour = Region.Name) +
  geom_point(shape = "circle", size = 2) +
  geom_line(size = 0.58, show.legend = NULL) +
  scale_color_manual(values = c(Niger = "#85C1E9", `Western Africa` = "#5B5B5C", World = "#B2BABB")) +
  labs(title = "Figure : Gender inequality index",
       caption = "Source : UNDP", x = NULL, y = NULL, color = NULL) +
  scale_x_continuous(breaks = seq(from = 1960, to = 2021, by = 10)) +
  scale_y_continuous(breaks = seq(from = 0, to = 0.8, by = 0.2), expand = c(0, 0)) +
  ylim(0, 0.8) +
  theme_minimal() +
  theme(
    plot.title = element_text(colour = "#0F4761", face = "italic", size = 9, vjust = 8),
    legend.text = element_text(size = 8),
    legend.position = c(0.132, 1.03),
    legend.direction = "horizontal",
    plot.caption = element_text(hjust = 0),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_line(linetype = "dashed"),
    plot.margin = margin(30, 30, 30, 30)
  ) +
  # Ajouter l'étiquette au point spécifique avec geom_label_s
  #Vjust et hjust place le label sur le point 
  geom_label_s(data = specific_value, aes(label = paste("World :", format(Value, digits = 2))), 
               x = 2010, y = specific_value$Value, vjust = 0, hjust =0, color = "black")

Autre alternative

On va utiliser la fonction ggplotly

p <-ggplot(base) +
      aes(x  = `Start Year`, y = Value, fill=Region.Name, colour = Region.Name) +
      geom_point(aes(text = paste(Region.Name, ": ",
                        round(Value,1))),shape = "bullet", size=2) +
      geom_line(size=1) +  
      scale_fill_hue(direction = 1) +  
      scale_color_manual(
      values = c(Niger = "#85C1E9",
      `western Africa` = "#5B5B5C",
      world = "#B2BABB")
      )+
      scale_fill_manual(
      values = c(Niger = "#85C1E9",
      `western Africa` = "#5B5B5C",
      world = "#B2BABB")
      )+
      labs(title = "Figure : Gender inequality index",caption = "Source : World Bank", x=NULL, y=NULL, color =NULL, fill=NULL)+
      geom_abline(intercept = 0, slope = 0, color="white")+
      scale_x_continuous(breaks=seq(from=1990, to = 2020, by=5))+
      scale_y_continuous(breaks = seq(0, 0.8, by =0.2),
                         labels=c("",seq(0.2,0.8, by=0.2)))+ 
       ylim(0,0.8)+
       theme_minimal()+
       theme(
         plot.title = element_text(face = "bold", size = 10,"italic", colour = "#85C1E9"),
         panel.grid.minor = element_blank(),
         panel.grid.major.y = element_line(linetype = "dashed")
         )

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

#Graphique dynamique
ggplotly(p, tooltip = "text")%>%
      plotly::layout(legend=list(x=0, #Paramètre pour la légende
                                y = 1.04,
                                 orientation='h'), annotations = 
                       #Paramètre pour la source
 list(x = 0.2, y = -0.08, text = "Source: UNDP", 
      showarrow = F, xref='paper', yref='paper', 
      xanchor='right', yanchor='auto', xshift=0, yshift=0,
      font=list(size=12, color="black")))